Foraging Causes and Consequences of Consumer Behavior

1
ForagingCauses and Consequences of Consumer
Behavior

• Peter B. McEvoy
• Insect Ecology
• Ent 420/520

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Consumer Behavior

1. Physiological ecology how organisms respond to
   food (e.g. visual, chemical cues)
2. Behavioral ecology how natural selection has
   favored particular patterns of consumer behavior
   in particular environments (e.g. optimal
   foraging)
3. Populations dynamics how behavior of predator
   and prey influence their population dynamics

3
Consumer Searching Behavior Contrasting Views
and Approaches

• Contrast nonrandom laboratory view and random
  field view (Morris and Kareiva 1991)
• Contrast phenomenological and mechanistic
  approaches (Turchin 1998)

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Physiological Ecology of Host-plant Selection A
Catenary Process

• Behavioral elements follow each other in fixed
  order or reaction chain
• random movement
• searching guided by olfactory and/or optical cues
• contact/evaluation using visual, olfactory,
  mechanosensory, gustatory information
• acceptance/rejection
• What cues give reliable information at each step?

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Generalized Sequence of Events in Host-plant
Selection
Schoonhoven et al 1998
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Behavioral Ecology of Consumer-Resource Relations

• Consumer Diets (specialization)
• Individual Behavior (functional, numerical,
  aggregative responses)
• From Individual Behavior to Population Dynamics
  (aggregation of risk in relation to resource
  density)
• Optimal Foraging Approach (critique of the
  adaptationist program)

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Responses of consumer to changes in resource
density

• Behavioral response within generation
• Functional response is a change in per capita
  consumption rate of consumer brought about by
  change in resource density
• Congregative response is movement of consumer
  leading to concentration of consumers in areas of
  high resource density
• Reproductive response between generations
• Numerical response is a change in per capita
  reproductive rate of consumer in response to
  change in abundance of resource

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Representing consumer responses in models
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Food Preferences of Consumers

• Food preferences. Necessary to examine both food
  in diet and availability of different food
  types in the environment. Availability must be
  seen not through the eyes of the observer but
  through the eyes of the insect itself.
• Ranked and balanced preferences. Ranked
  preferences predominate when food items can be
  classified on a single scale (many carnivores).
• Mixed diets. Many consumers exhibit a
  combination of ranked and balanced preferences
  (many herbivores). Performance is far better on a
  mixed diet than on a pure diet of even the best
  food.
• Switching. In contrast to a fixed preference,
  switching involves a preference for the more
  frequent food types.

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Concept Alert!

• Consumer preference vs performance. Resource use
  reflects both consumer choice (preference) and
  consumer ability to convert resource into useful
  outputs like growth and reproduction (performance)

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Host Specializationin Swallowtail butterfly
Papilio machaon
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Host SpecializationPreference and Performance in
Papilio machaon
Larval performance Most plants are suitable food
plants, but females lay eggs on only a fraction
Hierarchy of oviposition preferences Females lay
eggs on plants that do not support growth
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Oviposition by Papilio machaon occurs on plants
unsuitable for larvae

• Two invasive plant species, Bifora radians and
  Levisticum officinale, both Apiaceae
  (Umbelliferae), were introduced into Scandinavia
  and are rare oviposition behavior has not yet
  evolved to avoid these species

Bifora radians
Levisticum officinale
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Host range in taxonomically conservative

• Plants in subfamily Apiodeae suitable for larvae,
  those in Hydrocotyloideae and Saniculoideae
  resulted in 100 mortality

Saniculoideae (Sanicula, Eryngium),
Hydrocotyloideae (Hydrocotyle).
http//biodiversity.uno.edu/delta/angio/www/umbell
if.htm
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Host range in Lab gtgtHost Range in Field
Papilio machaon
Angelica archangelica ssp. littoralis

• Total of 33 spp. Apiaceae used in lab, only 1 sp
  (Angelica archangelica ssp. littoralis) used in
  field

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Summary Papilio machaon study

1. Invasive plant species. Oviposition on hosts
   unsuitable for larvae. Bifora radians and
   Levisticum officinale, both Apiaceae
   (Umbelliferae), were introduced into Scandinavia
   and are rare oviposition behavior not yet
   evolved to avoid these species
2. Host Range Taxonomically Conservative Plants in
   Apiodeae suitable for larvae, those in
   Hydrocotyloideae and Saniculoideae resulted in
   100 mortality
3. Host range in Lab gtgtHost Range in Field. Total
   of 33 spp. Apiaceae used in lab, only 1 sp
   (Angelica archangelica ssp. littoralis) used in
   field
4. Unsettled points. Preference and performance in
   larvae, abiotic and biotic factors in wild may be
   absent from the lab, host a habitat as well as
   food

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Pattern of Movement Behavior

• Key behavioral characteristics
• Frequency, the proportion of time spent moving
• Rate, the rate of movement when actually in
  motion
• Orientation, the direction of movement
• Three forms of movement
• Nonrandom settlement, to move or not to move,
  that is the question rate and orientation while
  moving are random
• Area restricted search refers to change in
  consumer searching behavior (increase in turning
  frequency and/or decrease in velocity) following
  feeding
• Orientation, consumer uses cues regarding
  neighboring plants (odors or visual cues) to
  alter direction of movement

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Insect Movement in Heterogeneous Space (Turchin
and Omland 1999)

• Distribution of food (directed movement or taxis,
  undirected movement or kinesis)
• Tracking abiotic factors
• Distribution of conspecifics (DD congregation,
  dispersal, mate finding)
• Avoiding predation
• Dealing with multiple factors

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How Herbivores Use Odors to Orient Toward Host
Plants Experimental Setup
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Orientation to Upwind Odor (External Stimuli)
Mediated by Satiation (Internal State)
Male
Female
Orientation when Starved
Random when Satiated
Ragwort flea beetle Longitarsus jacobaeae
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Taxis Directed Movement in Response to
OdorColorado Potato Beetle
(B) Pure tomato
(A) Clean air
(D) Mix tomato potato Associational resistance
(C) Pure potato
Note wind direction, mean vector length and
upwind component
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Analysis of Movement Pathsstep length/time
(velocity), turning rate, turning angle
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House Flies Tend to Remain Where Resources Are
Concentrated
Large turning angle, small step length
immediately after feeding, then return to ground
state
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Bumblebees Use Area Restricted Search
Low nectar, longer moves, lower turning angle
Bumblebees use Area Restricted Search
High nectar, shorter moves, higher turning angle
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Patch Residence Time in Colias Regulated by
Changing Step Length
Median percent cover
Median step length
Median step length
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Aphis varians Congregate by Increasing
Probability of Settling With Increasing Aphid
Density
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Functional Responses Consumption Rate X Food
Density

• Type 2 functional responses and handling time
• Parasitoid Pleolophus basizonus attacking cocoons
  of the European pine sawfly Neodiprion sertifer
• Type 2 response can be defined by handling time
  and searching efficiency. Hollings disc
  equation
• Alternative reasons for a type 2 functional
  response
• Type 1 functional response
• Type 3 functional response will arise whenever an
  increase in food density leads to an increase in
  the consumers searching efficiency or decrease
  its handling time

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Consequences of Functional Responses for the
Dynamics of Populations
Does functional response translate into direct
density dependence in mortality rate?
Prey killed per predator
Mortality Rate
Prey density
Prey density
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Effects of Consumer Density Mutual Interference
Leads to Reduction in Consumption Rate

• Coefficient of interference plotting log10
  searching efficiency as a function of consumer
  density. Slope takes the value of m and m is
  known as the coefficient of interference
• The reverse of interference is facilitation
• Mutual interference tends to stabilize
  predator-prey dynamics

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Consumers, Food Patches, and Aggregative Responses

• Eye of the beholder. Patch must be defined with a
  particular consumer in mind
• Definition. Patch is area within which
  homogeneity can be assumed
• Food value. Patches can vary in the density of
  food or prey they contain
• Predation risk. Risk of predation often varies
  among patches differing in density

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Aggregation of Risk Traditional View

• Some simple expectations
• Allocation of time. Consumers generally spend
  more time in patches containing high densities
  (because these are the most profitable patches)
• Distribution of consumers. Most consumers are
  therefore found in such patches
• Risk of being consumed. Prey in those patches
  are therefore more vulnerable to predation
  whereas those in low density-patches are
  relatively protected and more likely to survive
• Often contradicted by observations responses may
  be directly or inversely density dependent,
  domed, or density independent
• For herbivores (Cromartie 1975 Kareiva 1983)
• For parasitoids (Lessells 1985, Stiling 1987,
  Walde Murdoch 1988, Pacala Hassell 1991)

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Aggregation of Risk Modern View

• Causes of clumping. Herbivores often aggregate
  without this being an aggregative response
• Aggregation of risk. Heterogeneity in the risk of
  being consumed may reflect individual variation
  in resistance, timing, or spatial location

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How Flea Beetles Forage Among Patches Differing
in Plant Density

• Organisms. Flea beetle Longitarsus jacobaeae
  foraging among patches of its host plant ragwort
  Senecio jacobaea
• Methods. Employed mass mark-recapture methods
  because it is difficult to follow individuals
• Processes. Study includes colonization, birth,
  death, and interaction components
• Time scale assumptions. Assume colonization and
  redistribution process occurs on faster time
  scale than birth, death, and population
  interactions. Organisms move, then interact.
• Aggregation mechanisms. Diffusive, kinetic, and
  taxis mechanisms allow individual foragers to
  regulate their residence time in resource patches
  according to the quality of the patch

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Ragwort Biocontrol
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Life Cycle of Ragwort Flea Beetle
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Driving Forces

• Disturbance removes organisms, recycles
  limiting resources, sets the stage for
  colonization and occupancy
• Colonization movement to the resource mediated
  by random and nonrandom elements
• Local Interactions plant (insect) competition
  and insect herbivory

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Equilibrium Density of Foragers
Evaluated at equilibrium, proportional increase
in hosts causes lt proportional increase in
beetles (slope lt 1)
Beetle population reached equilibrium day 7
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Colonizing Abilities of 3 Insects Introduced for
Ragwort ControlProportion of patches occupied x
distance from source
Flea Beetle lt
Cinnabar moth lt
Seed-head fly
Proportion
Distance from Source
D50 Distance with probability of 50
infestation rate at origin
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Year to Year Variation in ColonizationProportion
of Plants Occupied X Distance From Source
D50 148 m
D50 119 m
Proportion declines with distance in one year
Proportion independent of distance the next year
D50 Distance with probability of 50
infestation rate at origin
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Factors Influencing ColonizationRagwort Flea
Beetle

• Temperature Cold snap (lt 5oC)
• Season Rise and fall of beetle numbers during
  colonizing phase
• Hosts per patch Amplitude of colonization curve
  increases with the number of hosts per patch
  (host area)
• Distance from source Amplitude of colonization
  curve grows weaker with distance from source

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Flea Beetles Emerging the Following Year

• Amplitude of beetle emergence curve increases
  with number of hosts per patch
• Cumulative number of beetles emerging increases
  with number of hosts per patch

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Growth Rate of Beetle Population
Allee Effect Decrease in growth rate at low
density
N(t1) / N(t)
Male-bias sex ratio in 1-plant patch, female
bias in larger patches
Males/Females
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Growth Rate of Plant PopulationHerbivore Effect
Independent of Plant Density
Beetles excluded
Beetles present
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Summary of Flea Beetle Study

• Evidence of consumer/resource equilibrium
  encourages focus on equilibrium rather than
  transient states
• Causes of aggregation highlight mix of random and
  nonrandom behavior (diffusion, kinesis, taxis)
• Consequences of aggregation for growth rates of
  consumer and plant populations, dynamics of
  interaction. No refuge from parasitism due to
  heterogeneity of risk.

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Quantitative Analysis of Foraging Movement
(Morris and Kareiva 1991, Turchin 1998, Turchin
and Omland 1999)

• Represent random and nonrandom components of
  searching behavior in a mathematical model
  (diffusion, kinesis, taxis)

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They Find Empirical Studies Are Not Measuring
the Right Things

• Current state of our knowledge. Empirical studies
  provide quantitative measurements of all three
  components of movement (diffusion, kinesis,
  taxis) in only 2 of 45 species (i.e. mite
  Tetranychus urticae and butterfly Pieris rapae)
• Consequences of our ignorance Failure to
  understand how insects locate superior food
  plants hampers investigation of
• Host race formation and evolution of diets
• Plant-herbivore coevolution
• Implications of different cropping patterns
• Safety and effectiveness of biocontrol organisms
• Consequences of changing food quality of
  herbivore populations for population dynamics

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Key Questions for Empirical StudiesMorris and
Kareiva

• Should I distinguish mobile and sedentary
  subpopulations? Should the foraging population be
  divided into mobile and sedentary subpopulations,
  and can it be treated as one pool of mobile
  individuals?
• How do I assess sensitivity to variation in host
  quality? Does the herbivore alter its rate of
  movement in accord with spatial variation in the
  quality of its potential host plants?
• How do I screen for orientation? Do individuals
  orient and move in a directed manner toward
  plants whose quality stands above the quality of
  surrounding vegetation?

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Discussion

1. What processes besides movement can lead to
   aggregation of consumers?
2. How might proponents of foraging theory respond
   to criticisms leveled by Lewontin and Gould?
3. Why are phenomenological models so difficult to
   relate to actual movement behavior?
4. Now that we have have mechanistically-grounded
   models, is there any need to employ
   phenomenological models lacking such a basis?

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Refuge Theory Now the Outcome of Biocontrol
(Y) Can Be Predicted From a Single, Easily
Measured Parameter (X) (Hold the hype)
Hawkins et al 1994
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Study System
Tachinid parasitoid, Tachinomyia similis of
western tussock moth (Orgyia vetusta Bdv.
Lymantriidae)
Bush lupine (Lupinus arboreus)
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Mechanisms leading to inverse-density dependence
in parasitism

• Leaving patches at a constant rate to avoid
  self-superparasitism
• Decelerating functional responses caused by
  behaviors such as handling time or group defenses
• Interference among parasitoids

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Alternative Models of Parasitism

1. No effect of host density on parasitism.
2. Weekly variability in parasitoid aggregation, no
   decelerating functional response.
3. Decelerating (type 2) functional response
4. Decelerating functional response and weekly
   variability in parasitoid aggregation

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Maximum Likelihood Method

• Used to fit a regression line to bivariate or
  multivariate data rather than the least-squares
  method
• Will result in values for the parameters that
  make the observed values in our data set seem
  most probable
• Solution is carried out iteratively on computer

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Akaike information criterion

• Criterion for selecting among models
• Best model is one with lowest AIC
• Compromise between minimizing the number of
  parameters and minimizing the residual variance
• The AIC is a number associated with each model
• AICln (sm2) 2m/T
• where m is the number of parameters in the model,
  and sm2 is (in an AR(m) example) the estimated
  residual variance sm2 (sum of squared
  residuals for model m)/T, and T the number of
  observations.

http//economics.about.com/cs/economicsglossary/g/
akaikes.htm
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Fig. 1.  Foraging Tachinomyia similis (fly)
population response to variation in Orgyia
vetusta (host) density.
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Fig. 2.  Attack rates by T. similis in relation
to experimentally manipulated density of O.
vetusta and T. similis.

• Host treatment effect (plt0.001)
• No Fly treatment effect (P0.73)

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Fig. 3.  Observed parasitism rates by T. similis
(fly) throughout the season in relation to
densities of larval O. vetusta (host).

• Model 2 (dashed) includes an effect of parasitoid
  aggregation
• Model 4 (solid) includes effect of both
  parasitoid aggregation and saturating functional
  response
• ß the increase in fly density expected with
  increases in host density
• s the strength of saturation in the functional
  response

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General Application

• maximizing Eq. 5 where the expected percent
  parasitism, pj, is described by the general form
  1expF(H,P)A(H,P)t where F(H,P) is the
  functional response of the predator in units of
  (parasitoid density time)-1 and A(H,P) is the
  aggregative response of the predator in units of
  parasitoid density and t is the length of the
  experiment.